#!/usr/bin/env python
#-*- encoding:utf-8 -*-
"""
Demonstrate how to create an interactive histogram, in which bars
are hidden or shown by cliking on legend markers.
The interactivity is encoded in ecmascript (javascript) and inserted in
the SVG code in a post-processing step. To render the image, open it in
a web browser. SVG is supported in most web browsers used by Linux and
OSX users. Windows IE9 supports SVG, but earlier versions do not.
Notes
-----
The matplotlib backend lets us assign ids to each object. This is the
mechanism used here to relate matplotlib objects created in python and
the corresponding SVG constructs that are parsed in the second step.
While flexible, ids are cumbersome to use for large collection of
objects. Two mechanisms could be used to simplify things:
* systematic grouping of objects into SVG tags,
* assingning classes to each SVG object according to its origin.
For example, instead of modifying the properties of each individual bar,
the bars from the `hist` function could either be grouped in
a PatchCollection, or be assigned a class="hist_##" attribute.
CSS could also be used more extensively to replace repetitive markup
troughout the generated SVG.
__author__="david.huard@gmail.com"
"""
import numpy as np
import matplotlib.pyplot as plt
import xml.etree.ElementTree as ET
from StringIO import StringIO
import json
plt.rcParams['svg.embed_char_paths'] = 'none'
# Apparently, this `register_namespace` method works only with
# python 2.7 and up and is necessary to avoid garbling the XML name
# space with ns0.
ET.register_namespace("","http://www.w3.org/2000/svg")
# --- Create histogram, legend and title ---
plt.figure()
r = np.random.randn(100)
r1 = r + 1
labels = ['Rabbits', 'Frogs']
H = plt.hist([r,r1], label=labels)
containers = H[-1]
leg = plt.legend(frameon=False)
plt.title("""From a web browser, click on the legend
marker to toggle the corresponding histogram.""")
# --- Add ids to the svg objects we'll modify
hist_patches = {}
for ic, c in enumerate(containers):
hist_patches['hist_%d'%ic] = []
for il, element in enumerate(c):
element.set_gid('hist_%d_patch_%d'%(ic, il))
hist_patches['hist_%d'%ic].append('hist_%d_patch_%d'%(ic,il))
# Set ids for the legend patches
for i, t in enumerate(leg.get_patches()):
t.set_gid('leg_patch_%d'%i)
# Set ids for the text patches
for i, t in enumerate(leg.get_texts()):
t.set_gid('leg_text_%d'%i)
# Save SVG in a fake file object.
f = StringIO()
plt.savefig(f, format="svg")
# Create XML tree from the SVG file.
tree, xmlid = ET.XMLID(f.getvalue())
# --- Add interactivity ---
# Add attributes to the patch objects.
for i, t in enumerate(leg.get_patches()):
el = xmlid['leg_patch_%d'%i]
el.set('cursor', 'pointer')
el.set('onclick', "toggle_hist(this)")
# Add attributes to the text objects.
for i, t in enumerate(leg.get_texts()):
el = xmlid['leg_text_%d'%i]
el.set('cursor', 'pointer')
el.set('onclick', "toggle_hist(this)")
# Create script defining the function `toggle_hist`.
# We create a global variable `container` that stores the patches id
# belonging to each histogram. Then a function "toggle_element" sets the
# visibility attribute of all patches of each histogram and the opacity
# of the marker itself.
script = """
"""%json.dumps(hist_patches)
# Add a transition effect
css = tree.getchildren()[0][0]
css.text = css.text + "g {-webkit-transition:opacity 0.4s ease-out;-moz-transition:opacity 0.4s ease-out;}"
# Insert the script and save to file.
tree.insert(0, ET.XML(script))
ET.ElementTree(tree).write("svg_histogram.svg")